import lightgbm as lgb
import time
 
 
params = {'max_bin': 63,
'num_leaves': 255,
'learning_rate': 0.1,
'tree_learner': 'serial',
'task': 'train',
'is_training_metric': 'false',
'min_data_in_leaf': 1,
'min_sum_hessian_in_leaf': 100,
'ndcg_eval_at': [1,3,5,10],
'sparse_threshold': 1.0,
'device': 'gpu',
'gpu_platform_id': 0,
'gpu_device_id': 0}
 
 
dtrain = lgb.Dataset('data/higgs.train')
t0 = time.time()
gbm = lgb.train(params, train_set=dtrain, num_boost_round=10,
          valid_sets=None, valid_names=None,
          fobj=None, feval=None, init_model=None,
          feature_name='auto', categorical_feature='auto',
          early_stopping_rounds=None, evals_result=None,
          verbose_eval=True,
          keep_training_booster=False, callbacks=None)
t1 = time.time()
 
print('gpu version elapse time: {}'.format(t1-t0))
 
 
params = {'max_bin': 63,
'num_leaves': 255,
'learning_rate': 0.1,
'tree_learner': 'serial',
'task': 'train',
'is_training_metric': 'false',
'min_data_in_leaf': 1,
'min_sum_hessian_in_leaf': 100,
'ndcg_eval_at': [1,3,5,10],
'sparse_threshold': 1.0,
'device': 'cpu'
}
 
t0 = time.time()
gbm = lgb.train(params, train_set=dtrain, num_boost_round=10,
          valid_sets=None, valid_names=None,
          fobj=None, feval=None, init_model=None,
          feature_name='auto', categorical_feature='auto',
          early_stopping_rounds=None, evals_result=None,
          verbose_eval=True,
          keep_training_booster=False, callbacks=None)
t1 = time.time()
 
print('cpu version elapse time: {}'.format(t1-t0))